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Adams - N and AvgCap
data.colorado.gov | Last Updated 2024-05-01T19:38:57.000ZThis dataset includes all non-24 hour licensed child care facilities in the State of Colorado. It is updated monthly, and is intended for public use. It includes CDHS-issued child care license numbers, legal business names as they appear in the licensing application, the types of service the programs provide, the physical location addresses of the programs as they appear in the licensing application, the longitude-latitude coordinate values derived from geocoding services and spatial QA, the valid Colorado Shines quality rating levels (if applicable), total licensed capacities, and CCCAP utilization and fiscal agreement. Note: As of Jan 1, 2021, the following fields are temporarily unavailable in this release: `CCCAP CHILD COUNT_D1`, `CCCAP CASE COUNT_D1`, and `CCCAP_AMOUNT_PAID_D1`. These columns will be included again in the near future. Please contact the dataset owner for more information as necessary. Disclaimer: The State of Colorado, the Colorado Department of Human Services, and the Office of Early Childhood make no representations or warranties expressed or implied, with respect to the use of data provided herewith regardless of its format or the means of its transmission. There is no guarantee or representation to the user as to the accuracy, currency, suitability, or reliability of this data for any purpose. The user accepts the data “as is”. The State of Colorado assumes no responsibility for loss or damage incurred as a result of any user reliance on this data. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information. The State of Colorado does not necessarily endorse any interpretations or products derived from the data.
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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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Fire / EMS Heat Map FY12 to Present
data.cityofgainesville.org | Last Updated 2023-09-28T14:19:17.000ZFire / EMS response data for FY2012 up to the most current month available.
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Uninsured Population Census Data 1-year estimates 2017-Current Statewide Human Services and Insurance
data.pa.gov | Last Updated 2022-02-21T19:25:46.000ZThe American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. This dataset provides estimates for Health Insurance Coverage in Pennsylvania and is summarized from summary table S2701: SELECTED CHARACTERISTICS OF HEALTH INSURANCE COVERAGE IN THE UNITED STATES. A blank cell within the dataset indicates that either no sample observations or too few sample observations were available to compute the statistic for that area. Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level. While an ACS 1-year estimate includes information collected over a 12-month period, an ACS 5-year estimate includes data collected over a 60-month period. In the case of ACS 1-year estimates, the period is the calendar year (e.g., the 2015 ACS covers the period from January 2015 through December 2015).
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Maternal Opioid Use Hospital Stays 2016-2017 County Health Care Cost Containment Council (PHC4)
data.pa.gov | Last Updated 2022-10-17T20:23:36.000ZCountywide counts of maternal hospital stays with opioid use and countywide rates of maternal hospital stays with opioid use per 1,000 maternal stays. Maternal stays include those involving a delivery, as well as other pregnancy-related stays. Opioid use, or opioid use disorder, is a diagnosis indicating opioid dependence, abuse, or use. Some opioid drugs may be prescribed as part of medication-assisted treatment to relieve withdrawal symptoms and psychological cravings often associated with opioid use disorders. Opioid use during pregnancy can lead to Neonatal Abstinence Syndrome (NAS) for newborns. This analysis is restricted to maternal hospital stays for Pennsylvania-state residents who were hospitalized in Pennsylvania hospitals. Disclaimer: PHC4’s database contains statewide hospital discharge data submitted to PHC4 by Pennsylvania hospitals. Every reasonable effort has been made to ensure the accuracy of the information obtained from the Uniform Claims and Billing Form (UB-82/92/04) data elements. Computer collection edits and validation edits provide opportunity to correct specific errors that may have occurred prior to, during or after submission of data. The ultimate responsibility for data accuracy lies with individual providers. PHC4 agents and staff make no representation, guarantee, or warranty, expressed or implied that the data received from the hospitals are error-free, or that the use of this data will prevent differences of opinion or disputes with those who use published reports or purchased data. PHC4 will bear no responsibility or liability for the results or consequences of its use.
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CL Apartment new Bldg
data.lacity.org | Last Updated 2023-05-22T09:33:24.000ZThe Department of Building and Safety issues permits for the construction, remodeling, and repair of buildings and structures in the City of Los Angeles. Permits are categorized into building permits, electrical permits, and mechanical permits (which include plumbing, HVAC systems, fire sprinklers, elevators, and pressure vessels). Depending on the complexity of a project, a permit may be issued the same day with Express Permit or e-Permit ("No Plan Check" category), or a permit may require that the plans be reviewed ("Plan Check" category) by a Building and Safety Plan Check personnel.
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New Building Commercial
data.lacity.org | Last Updated 2023-05-22T09:33:24.000ZThe Department of Building and Safety issues permits for the construction, remodeling, and repair of buildings and structures in the City of Los Angeles. Permits are categorized into buildings permits, electrical permits, and mechanical permits (which include plumbing, HVAC systems, fire sprinklers, elevators, and pressure vessels) . Depending on the complexity of a project, a permit may be issued the same day with Express Permit or e-Permit ("No Plan Check" category), or a permit may require that the plans be reviewed ("Plan Check" category) by a Building and Safety Plan Check personnel.
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Animal Contact Exhibits_Legal Epidemiology Research Procedure and Code Book_2016
data.cdc.gov | Last Updated 2017-06-27T09:28:31.000ZAnimals at petting zoos and agricultural fairs can be carriers of pathogens, such as Escherichia coli. Disease outbreaks at animal contact exhibits can be prevented by handwashing after contact with animals and keeping food and beverage away from exhibits. This research procedure and code book accompanies the data set, Animal Contact Exhibits_Legal Epidemiology Dataset_2016, which catalogs and analyzes a collection of state hand sanitation laws for the following categories of animal contact exhibits: a. Petting zoos b. Agricultural fairs c. County or state fairs d. Exotic animal exhibits e. Circuses f. Zoos
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Classification of Aeronautics System Health and Safety Documents
data.nasa.gov | Last Updated 2020-01-29T01:57:57.000ZMost complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades and contains over 700 000 reports. The Aviation Safety Action Plan (ASAP) contains over 12 000 reports from various airlines. Problem categorizations have been developed for both ASRS and ASAP to enable identification of system problems. However, repository volume and complexity make human analysis difficult. Multiple experts are needed, and they often disagree on classifications. Even the same person has classified the same document differently at different times due to evolving experiences. Consistent classification is necessary to support tracking trends in problem categories over time. A decision support system that performs consistent document classification quickly and over large repositories would be useful. We discuss the results of two algorithms we have developed to classify ASRS and ASAP documents. The first is Mariana---a support vector machine (SVM) with simulated annealing, which is used to optimize hyperparameters for the model. The second method is classification built on top of nonnegative matrix factorization (NMF), which attempts to find a model that represents document features that add up in various combinations to form documents. We tested both methods on ASRS and ASAP documents with the latter categorized two different ways. We illustrate the potential of NMF to provide document features that are interpretable and indicative of topics. We also briefly discuss the tool that we have incorporated Mariana into in order to allow human experts to provide feedback on the document categorizations.
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Annual Report on Outreach to and Training of Cosmetologists (Historical)
data.cityofnewyork.us | Last Updated 2024-01-31T18:59:34.000ZThis data is from an annual report to be provided in compliance of Local Law 39 of 2019, covering the time period July 1 through October 15. The data set includes: a summary of outreach efforts to the cosmetology community, including the number of trainings provided for cosmetologists, disaggregated by borough. For Data Dictionary, please refer to this <a href="https://docs.google.com/spreadsheets/d/1P0b17twfrYTBfGN7J3jFV-pVV_H3nlkLITVz_8GmmNc/edit#gid=0">link</a>.